Several members of the Identity and Fraud Solutions team recently attended the Global Identity Summit in Tampa. Rich Huffman, vice president of identity and fraud product management, spoke on a panel about how government entities are leveraging data when verifying identities to increase security and decrease fraud.
The federal government identifies millions of people every day for many reasons. For instance, last year, 14 million people applied for and received passports. In addition, the government has to confirm the identities of the millions of people who receive Medicare and Social Security benefits before distributing resources to them.
The panel, including Ryan Fox of CapitalOne and Helen Schmitz of Homeland Security, discussed how government programs, faced with ever growing fraud risks, are employing best practices in identity proofing to verify people and to ensure the person is eligible for the program in question. Here are some of the steps the panel recommended to do just that.
Ask the Right Questions
As the government reviews an online application, the most important step is to determine the person is, indeed, “real”, and who they claim to be.
When evaluating the identity of a person, the first step is looking at identity elements (name, address, SSN, DOB, etc.) to confirm that an identity corresponds with a real human. Additional steps, such as questions about “what you know” or information about “what you have” should be posed in a way that can help uncover potential identity theft. Once the identity has been confirmed, the government entity in play must make sure the person qualifies for the program or benefit for which they are applying. This may include looking at household demographics, employment, undisclosed liabilities from the applicant, and criminal background.
The best way to confirm identity is to compare the information given in an application to existing, established data. Data coming from multiple sources can be conflicting, so best practice will link all this information back to already confirmed data about the person. After validating the data and evaluating its trustworthiness when coming from a remote device, the next step is incorporating background analytics like user behavior patterns, velocity checks, and potentially device recognition to help identify the potential for fraud.
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